Abstract 3125: TCR specificity prediction of circulating T cells and TILs in personalized adoptive neoTCR T cell therapy
Authors: Tyler Borrman, Eric Stawiski, Zheng Pan, Chad Smith, Susan Foy, Stefanie J. Mandl
Journal: Cancer Research (2023)

Abstract Background: Adoptive T-cell therapies would benefit from accurate computational prediction of T cell receptor (TCR) specificity to its target antigen. Despite the diversity of complementarity determining region (CDR) among T cells, many sequence features of CDRs are conserved. Conserved CDR sequences allow for shared specificity, that is, recognition of the same antigen, potentially increasing the pool of candidate TCRs available for treating patients. Utilizing sequence similarity, enrichment of V-genes, CDR lengths, and evidence of clonal expansion, several computational algorithms have been developed to predict the specificity of TCRs. Using neoantigen-specific TCRs (neoTCRs) in our phase 1 clinical trial (NCT039703820), we investigated the shared specificity of TCRs in the context of personalized autologous T cell therapy for cancer patients. Methods: Leveraging a high throughput TCR discovery and validation platform, neoantigen-specific T cells were isolated from patient peripheral blood mononuclear cells (PBMCs) and their neoTCR sequences were identified. RNA-seq was performed on tumor biopsies and TCR sequences derived from tumor-infiltrating lymphocytes (TILs) were extracted using MiXCR software. TCR specificity algorithms TCRdist3, GLIPH2, and TCRmatch were then used to identify specificity groups within and between neoTCR sequences derived from patient PBMCs, TCR sequences derived from TILs, and publicly available TCR sequences of known specificities. Results: NeoTCRs identified during our phase 1 clinical trial and their known neoantigens provided an experimentally validated benchmark for testing accuracy of TCR specificity prediction. TCR specificity algorithms accurately clustered neoTCR β chains into groups of shared antigen specificity. TCR chain sequences detected from TILs with high similarity to TCR sequences of neoTCRs from PMBCs were found, suggesting shared antigen specificity between circulating T cells and those found in the tumor. In addition, TCR specificity algorithms identified a public TCR sequence known to recognize an HPV-derived epitope presented by HLA-A*02:01 with high similarity to a TCR sequence identified in a tumor biopsy of an HPV16+ HLA-A*02:01+ patient with head and neck cancer. Conclusion: Neoantigens and their associated neoTCRs identified by our TCR discovery and validation platform can be used as a benchmark for TCR specificity prediction algorithms. TCR specificity algorithms provide insight into TCRs with predicted shared specificity in the blood and within the tumor. Accurate identification of TIL TCRs with shared specificity to neoTCRs could inform on tumor trafficking and aid in therapeutic product selection. Similarly, accurate specificity matching of TIL TCRs to public TCRs with known targets could aid in identification of efficacious targets within the tumor. Citation Format: Tyler Borrman, Eric Stawiski, Zheng Pan, Chad Smith, Susan Foy, Stefanie J. Mandl. TCR specificity prediction of circulating T cells and TILs in personalized adoptive neoTCR T cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3125.